1. Field of the Invention
The present invention relates to a system for tracking a moving object, such as a man, by utilizing particle filtering.
2. Description of the Related Art
A system has been developed, which processes video data acquired by a camera, thereby tracking an object, such as a walking man or any other moving object. (See, for example, Jpn. Pat. Appln. KOKAI Publication No. 2003-216951.) In recent years, a system for tracking a moving object by means of an algorithm called “particle filtering” has been exploited. (See, for example, Jpn. Pat. Appln. KOKAI Publication No. 2005-165688.).
In a tracking method utilizing particle filtering, particle filtering is performed. Particle filtering comprises four processes: initialization, prediction, likelihood inference and filtering. The tracking method can track a moving object, without the necessity of describing a motion model of the object. In a practical example of the tracking method, a moving object is regarded as a group of particles, and the particles constituting the group are tracked, thereby to track the moving object.
The conventional method of tracking a moving object is to track the region in which the object exists, or to detect this region by using the moving object (e.g., silhouette model) (or to perform an initialization process) and then to interpret the region continuously, thereby to track the moving object. In the conventional method, a motion model of the object may be introduced into the region extracted as a candidate region in which the moving object may exist, in order to increase the ability of detecting the region in which the object exists. Even if the motion model is used, however, the ability of tracking that region may decrease if the candidate region changes in shape, from time to time, due to the moving object as seen (at a specific camera view angle) or to the ability of detecting that region.
Any tracking method that utilizes particle filtering is advantageous because it can track a moving object without describing a motion model of the object. The tracking method is disadvantageous, however, in the following respects.
As mentioned above, the tracking system using the particle filtering is composed of four processes, i.e., initialization, prediction, likelihood inference and filtering. In the initialization process, the moving object (e.g., a man) is detected (extracted) and particles are then initially arranged.
The moving object may be detected by using a single-lens camera. In this case, however, the object can hardly be detected if the background is complicated and many other objects exist. Further, any other moving object cannot be well tracked based on the initial value acquired of the first moving objected detected if the initial value is not so accurate as desired. In view of this, the moving object must not be detected in the initialization process, or some measures must be taken to increase the ability of detecting the object.
In the prediction process, a system model defined beforehand is used. The system model may be influenced by the background, particularly if the object to track is a walking man. This is because the number of particles defining the walking man decreases due to the so-called random walk of the man (which depends on a random-walk parameter). That is, the system model can indeed be compatible with the random walk of a man, but may probably be arranged outside the man to be tracked. Inevitably, the system model may be located at a wrong position if the background is found to have high likelihood in the likelihood inference process.
In a process of rearranging the particles, so-called “occlusion” may occur if several people, for example, are photographed overlapping any other by a camera. Occlusion is a visual phenomenon in which the image photographed shadowed, in part or entirety, and cannot be recognized. If the occlusion takes happens, only the person standing foremost will have high likelihood. Consequently, the particles may be arranged densely at this person.
If the tracking method using the particle filtering is started while the occlusion is occurring, no particles are arranged at any person standing behind and thus shadowed even after the occlusion has been cancelled. That is, if occlusion occurs in the initialization process, only the person standing foremost will be tracked, whereas any person standing behind this person will not be tracked at all.